JP2018136337A - Tracking processing device and tracking processing method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To accurately track a tracking target regardless of a surrounding environment.SOLUTION: A tracking processing device 3 is configured to include: a tracking processing part 11 for performing processing of tracking a tracking target; and a congestion degree calculating part 22 for calculating a congestion degree of objects located within an area including an estimated position of the tracking target, where the congestion degree is a degree of congestion. The tracking processing part 11 sets a gain so that as a value of the congestion degree calculated by the congestion degree calculating part 22 is lower, tracking ability to the tracking target becomes higher.SELECTED DRAWING: Figure 1

Description

  The present invention relates to a tracking processing device and a tracking processing method for tracking a tracking target.

  As a conventionally known tracking processing device, for example, in the tracking processing device disclosed in Patent Document 1, the motion state of a target as a tracking target is estimated, and the target is tracked based on the motion state. Thereby, the target to be tracked can be accurately detected from the plurality of targets.

JP 2014-89056 A

  By the way, when the target to be tracked is tracked as described above, the target may not be accurately tracked depending on the environment around the target.

  The present invention is to solve the above-described problems, and an object thereof is to accurately track a tracking target regardless of the surrounding environment.

  (1) In order to solve the above problem, a tracking processing device according to an aspect of the present invention includes a tracking processing unit that performs processing for tracking a tracking target, and an object that is present in an area including the predicted position of the tracking target. A congestion degree calculation unit that calculates a degree of congestion that is a degree of congestion of the target, and the tracking processing unit follows the tracking target as the value of the congestion degree calculated by the congestion degree calculation unit decreases. It is characterized in that the gain is set so as to increase the performance.

  (2) Preferably, the tracking processing unit sets the gain so that the followability to the tracking target increases stepwise as the value of the congestion level calculated by the congestion level calculation unit decreases. It is characterized by.

  (3) Preferably, the said congestion degree calculation part calculates the said congestion degree based on the target number as the number of the said target which exists in the said area.

  (4) More preferably, the tracking processing device counts the target number that exists in each of a plurality of cells into which the area is divided, and the target number counted for each of the plurality of cells, Further comprising an echo distribution generation unit that stores data corresponding to each cell, and the congestion level calculation unit calculates the congestion level of the target existing in the cell including the predicted position of the tracking target. .

  (5) Preferably, the said congestion degree calculation part calculates the said congestion degree based on the said target number in several timing.

  (6) Preferably, the congestion level calculation unit calculates a smooth congestion level based on the congestion levels at a plurality of timings, and the tracking processing unit calculates the smooth congestion level calculated by the congestion level calculation unit. In accordance with the value of, the tracking target is tracked.

  (7) In order to solve the above problem, a tracking processing method according to an aspect of the present invention includes a step of performing processing of tracking a tracking target, and a target existing in an area including the predicted position of the tracking target. Calculating the degree of congestion that is a degree of congestion, and performing the tracking process includes following the tracking target as the value of the degree of congestion calculated by the step of calculating the degree of congestion is lower. It is characterized in that the gain is set so as to increase the performance.

  According to the present invention, it is possible to accurately track a tracking target regardless of the surrounding environment.

1 is a block diagram of a radar apparatus including a tracking processing apparatus according to an embodiment of the present invention. It is a typical top view for demonstrating the relationship between the own ship and a target object echo image. It is a data list for demonstrating the data extracted regarding a target echo image (target). It is a typical top view which shows the target echo image detected by the echo detection part. It is a flowchart for demonstrating operation | movement of a congestion degree calculation process part. It is a figure which shows typically the echo distribution produced | generated by the echo distribution production | generation part. It is a schematic diagram for demonstrating the calculation method of the congestion degree calculated by the congestion degree calculation part of the tracking processing apparatus which concerns on a modification.

  Hereinafter, an embodiment of a tracking processing device 3 according to the present invention will be described with reference to the drawings. The present invention can be widely applied as a tracking processing apparatus that tracks a target selected as a tracking target. Hereinafter, a target set as a tracking target is referred to as a “tracking target”. In the following, the same or corresponding parts in the drawings are denoted by the same reference numerals, and description thereof will not be repeated.

  FIG. 1 is a block diagram of a radar apparatus 1 including a tracking processing apparatus 3 according to an embodiment of the present invention. The radar apparatus 1 according to the present embodiment is a marine radar provided in a vessel such as a fishing boat. The radar apparatus 1 is mainly used for detecting a target such as another ship. Further, the radar apparatus 1 is configured to be able to track a target selected as a tracking target. The radar apparatus 1 is configured to be able to track a plurality of tracking targets at the same time. The radar apparatus 1 is configured to estimate the motion state of the tracking target. In the present embodiment, the radar apparatus 1 calculates the smoothing speed of the tracking target as the motion state. The smoothing speed is a vector indicating the traveling direction and the traveling speed estimated for the tracking target. The radar apparatus 1 displays the smoothing speed of the tracking target on the screen. Hereinafter, a ship provided with the radar device 1 is referred to as “own ship”.

  As shown in FIG. 1, the radar device 1 includes an antenna unit 2, a tracking processing device 3, and a display 4.

  The antenna unit 2 includes an antenna 5, a receiving unit 6, and an A / D conversion unit 7.

The antenna 5 is a radar antenna capable of transmitting a pulsed radio wave having strong directivity. The antenna 5 is configured to receive an echo signal that is a reflected wave from a target. That is, the echo signal of the target is a reflected wave at the target with respect to the transmission signal from the antenna 5. The radar apparatus 1 measures the time from when a pulsed radio wave is transmitted to when an echo signal is received. Thereby, the radar apparatus 1 can detect the distance r to the target. The antenna 5 is configured to be able to rotate 360 ° on a horizontal plane. The antenna 5 is configured to repeatedly transmit and receive radio waves while changing the transmission direction of pulsed radio waves (changing the antenna angle). With the above configuration, the radar apparatus 1 can detect a target on a plane around the ship over 360 °.

  In the following description, an operation from transmission of a pulsed radio wave to transmission of the next pulsed radio wave is referred to as “sweep”. The operation of rotating the antenna 360 ° while transmitting / receiving radio waves is called “scan”. In the following, a certain scan is referred to as an “n scan”, and a scan immediately before the n scan is referred to as an “n−1 scan”. Note that n is a natural number. In the following, the operation of the radar apparatus 1 at the n-scan time point will be described unless the operation is particularly described.

  The receiving unit 6 detects and amplifies the echo signal received by the antenna 5. The reception unit 6 outputs the amplified echo signal to the A / D conversion unit 7. The A / D converter 7 samples an analog echo signal and converts it into digital data (echo data) consisting of a plurality of bits. Here, the echo data includes data for specifying the intensity (signal level) of the echo signal received by the antenna 5. The A / D converter 7 outputs the echo data to the tracking processing device 3.

  The tracking processing device 3 identifies a target selected from a plurality of targets as a tracking target, and performs a tracking filter process on an observation position obtained by observing the tracking target, thereby tracking the target It is configured to perform a tracking process for tracking the target. More specifically, the tracking processing device 3 is configured to calculate the smoothing speed of the tracking target, the estimated position (smooth position) of the tracking target, and the like.

  The tracking processing device 3 is configured using hardware including a CPU, a RAM, a ROM (not shown), and the like. The tracking processing device 3 is configured by using software including a tracking processing program stored in the ROM.

  The tracking processing program is a program for causing the tracking processing device 3 to execute the tracking processing method according to the present invention. The hardware and software are configured to operate in cooperation. Thereby, the tracking processing device 3 can function as the signal processing unit 9, the echo detection unit 10, the tracking processing unit 11, and the like. The tracking processing device 3 is configured to perform processing described below for each scan.

  The tracking processing device 3 includes a signal processing unit 9, an echo detection unit (detection unit) 10, a tracking processing unit 11, and a congestion degree calculation processing unit 20.

  The signal processing unit 9 removes interference components included in the echo data and unnecessary waveform data by performing filter processing or the like. The signal processing unit 9 is configured to detect feature information of echo data related to the target echo image. The signal processing unit 9 outputs the processed echo data to the echo detection unit 10.

  The echo detection unit 10 is configured to detect a target echo image and detect feature information of echo data related to the target echo image. That is, the echo detection unit 10 includes a target echo image detection unit and a feature information extraction unit. The signal processing unit 9 and the echo detection unit 10 constitute a detection unit for detecting target feature information.

  The echo detector 10 obtains the distance r to the position corresponding to the echo data based on the read address when the echo data is read from the signal processor 9. Further, data indicating which direction the antenna 5 is currently facing (antenna angle θ) is output from the antenna 5 to the echo detector 10. With the above configuration, when the echo detection unit 10 reads the echo data, the echo detection unit 10 can acquire the position corresponding to the echo data with polar coordinates of the distance r and the antenna angle θ.

  The echo detector 10 is configured to detect whether or not a target exists at a position corresponding to the echo data. For example, the echo detector 10 determines the signal level at the position corresponding to the echo data, that is, the signal intensity. The echo detector 10 determines that a target exists at a position where the signal level is equal to or higher than a predetermined threshold level value.

  Next, the echo detector 10 detects a range where the target is present. For example, the echo detection unit 10 detects a group of areas where the target is present as an area where the target echo image is present. In this way, the echo detector 10 detects the target echo image based on the echo data. The outline shape of the target echo image substantially matches the outline shape of the target. However, due to noise included in the echo data, the outline shape of the target echo image is slightly different from the outline shape of the target. Next, the echo detector 10 extracts feature information related to the target echo image using the echo data.

  FIG. 2 is a schematic plan view for explaining the relationship between the ship 100 and the target echo image 120. In FIG. 2, the target echo image 120 is illustrated as a rectangular image. In FIG. 2, a target 130 specified by the target echo image 120 is shown. In FIG. 2, the outline shape of the target 130 is displayed in a state that matches the target echo image 120.

  As shown in FIGS. 1 and 2, in the polar coordinate system, a straight line distance from the ship position M1 is indicated as a distance r with respect to the ship position M1 as the position of the ship 100. Is shown as an angle θ. In the present embodiment, the ship position M <b> 1 corresponds to the position of the antenna 5. When extracting the representative point P of the target echo image 120, the echo detection unit 10 uses the partial image 110 of the ring-shaped portion centered on the ship position M1. The image 110 is an image of a region surrounded by the first straight line 111, the second straight line 112, the first arc 113, and the second arc 114.

  The first straight line 111 is a straight line passing through the point closest to the own ship position M1 in the rear edge 120a of the target echo image 120 and the own ship position M1. The second straight line 112 is a straight line passing through the point closest to the ship position M1 in the front edge 120b of the target echo image 120 and the ship position M1. The first arc 113 is an arc passing through the portion 120c closest to the ship position M1 in the target echo image 120. The center of curvature of the first arc 113 is the ship position M1. The second arc 114 is an arc passing through the portion 120d farthest from the ship position M1 in the target echo image 120. The second arc 114 is concentric with the first arc 113.

FIG. 3 is a data list for explaining data extracted with respect to the target echo image 120 (target 130). As shown in FIGS. 2 and 3, in the present embodiment, the signal processing unit 9 and the echo detection unit 10 cooperate with each other to obtain the following 12 pieces of data for the target echo image 120 (target 130): Extracted as feature information data. That is, the echo detector 10 extracts the following 12 pieces of text data based on the echo data and the image data of the target echo image 120. In the present embodiment, the twelve text data are flag data 201, distance rp data 202, end angle θe data 203, angular width θw data 204, and leading edge distance rn data 205. , Data 206 of the last edge distance rf and area ar (shape information)
Data 207, coordinate data 208 of the representative point P, echo level ec data 209, adjoining distance (information specifying the surrounding state of the target) ad data 210, Doppler shift amount ds data 211, , Data 212 at time tm.

  The above flag is a flag for specifying whether or not the target echo image 120 is in a predetermined state, for example. This flag is configured to be set to “1” or “0”.

  The distance rp is a linear distance from the ship position M1 to the representative point P of the target echo image 120. In the present embodiment, the representative point P is the center point of the image 110. The end angle θe is the antenna angle θ described above at the time when the detection of the target echo image 120 is completed. The angle width θw is the width of the target echo image 120 in the angular direction around the ship position M1. The angle width θw is also an angle formed by the first straight line 111 and the second straight line 112. The forefront edge distance rn is the distance between the portion 120c of the target echo image 120 and the ship position M1. The last edge distance rf is the distance between the portion 120d of the target echo image 120 and the ship position M1. The area ar is an area in the partially shaped image 110 of the ring-shaped portion, and is treated as the area of the target echo image 120 in the present embodiment.

  The echo level ec indicates the intensity of an echo signal that identifies the target echo image 120. This intensity may be a peak intensity of an echo signal specifying the target echo image 120, or may be an average value of the intensity of the echo signal. In the present embodiment, the adjacent distance ad is, for example, a distance between two adjacent target echo images 120. The Doppler shift amount ds is, for example, the difference between the frequency of the pulse signal radiated from the antenna 5 and the frequency of the echo signal reflected by the target 130 specified by the target echo image 120. Based on the Doppler shift amount ds, the relative speed between the target 130 specified by the target echo image 120 and the ship 100 can be obtained. Time tm is the time when the target echo image 120 is detected. Note that the data of the echo image 120 may include a spare data area. In the present embodiment, the signal processing unit 9 and the echo detection unit 10 extract the 12 pieces of feature information for each target echo image 120. These twelve pieces of feature information are information represented by numerical values.

  An example of a plurality of target echo images 120 detected by the echo detector 10 is shown in FIG. FIG. 4 is a schematic plan view showing the target echo image 120 detected by the echo detector 10. In FIG. 4, as an example, four target echo images 120 (121, 122, 123, 124) at the time of n scanning are shown. In FIG. 4, the shape of the target echo image 120 (121, 122, 123, 124) matches the shape of the target 130 (131, 132, 133, 134), respectively.

  The target 131 specified by the target echo image 121, the target 132 specified by the target echo image 122, and the target 133 specified by the target echo image 123 are, for example, small ships. The target 134 specified by the target echo image 124 is, for example, a large ship. The echo detection unit 10 includes the representative point P1 (n) of the target echo image 121, the representative point P2 (n) of the target echo image 122, and the target echo image 123 as the representative point P at the n scan time point. The representative point P3 (n) and the representative point P4 (n) of the target echo image 124 are detected. Hereinafter, a case where the target 131 is the tracking target 140 will be described as an example.

  As shown in FIGS. 1 to 4, the echo detection unit 10 outputs feature information data 201 to 212 for each target echo image 120 to the tracking processing unit 11 and the congestion degree calculation processing unit 20.

[Configuration of Congestion Calculation Processing Unit]
FIG. 5 is a flowchart for explaining the operation of the congestion degree calculation processing unit 20. Hereinafter, the configuration and operation of the congestion degree calculation processing unit 20 will be described with reference to FIGS. 1 and 5 and the like.

  The congestion degree calculation processing unit 20 is configured to calculate the degree of congestion (congestion degree) of a target existing around the predicted position Xp (n) of the tracking target 140. The congestion level calculation processing unit 20 includes an echo distribution generation unit 21 and a congestion level calculation unit 22. As will be described later in detail, the congestion degree calculation processing unit 20 receives the predicted position Xp (n) at the time of n scans of the tracking target calculated by the tracking processing unit 11.

  FIG. 6 is a diagram schematically illustrating the echo distribution generated by the echo distribution generation unit 21. In the following, the number of targets (target numbers) included in the cell B whose position on the xy coordinates shown in FIG. 6 is (x, y) during n scans will be described as N (x, y, n). . For example, referring to FIG. 6, N (1, 5, n) = 3. In FIG. 6, the counted target numbers are illustrated by black dots. In the following, an example in which the coordinate system is an xy coordinate will be described.

  The echo distribution generation unit 21 generates a distribution of each target 130 detected by the echo detection unit 10. Specifically, in the echo distribution generation unit 21, referring to FIG. 6, the number of targets 130 existing in each of a plurality of cells B obtained by dividing a predetermined area A on the sea into a lattice shape is expressed as cell B. It is counted every time (step S1 in FIG. 5). At this time, when a smooth target (which will be described in detail later) is not stored corresponding to each cell B (No in step S2), the echo distribution generation unit 21 counts the target 130 counted for each cell B. Are stored in correspondence with each cell B as a smooth target (step S3). In addition, as the area A, for example, a square area having a side of 64 miles can be cited as an example. Further, as the cell B, for example, a square region having a side of 0.5 mile can be cited as an example. In addition, in this embodiment, although the example which counts the target number for every cell B is given and demonstrated, it is not restricted to this, For example, the value which totaled the area of the target contained in each cell B as an example May be used instead of the above-mentioned target numbers.

  On the other hand, when the smooth target characteristic is stored corresponding to each cell B (Yes in step S2), the echo distribution generation unit 21 stores the smooth target characteristic Ns (corresponding to each cell B). Based on x, y, n-1) and the latest target N (x, y, n), a smooth target Ns (x, y, n) is calculated (step S4). Specifically, the echo distribution generation unit 21 calculates a smooth target characteristic Ns (x, y, n) based on the following equation (1).

Ns (x, y, n) = a ₁ · N (x, y, n) + (1−a ₁ ) · Ns (x, y, n−1) (1)

Here, a ₁ is a coefficient represented by 0 <a ₁ ≦ 1, for example 0.9 as an example.

  The echo distribution generation unit 21 then calculates the smooth target characteristic Ns (x, x) that has been calculated from the most recent smooth target characteristic Ns (x, y, n-1) corresponding to each cell B. , Y, t) and stored (step S5).

  The congestion degree calculation unit 22 calculates the degree of congestion C (n) using the smooth target characteristic Ns (x, y, n) of the cell B including the predicted position Xp (n) of the tracking target 140 (step) S6). The congestion degree C (n) can be expressed as an arbitrary function having the smooth target characteristic Ns (x, y, n) as a variable, that is, C (n) = f (Ns (x, y, n)). it can. An example of this equation is C (n) = Ns (x, y, n). Then, the congestion degree calculation unit 22 is the congestion degree of the cell B calculated in the past, and the smoothness degree Cs (n−1) stored corresponding to the cell B, and the congestion calculated most recently. Based on the degree C (n), the smoothness congestion degree Cs (n) is calculated (step S7). Specifically, the congestion degree calculation unit 22 calculates a smooth congestion degree Cs (n) based on the following equation (2). Then, the congestion degree calculation unit 22 replaces the previously stored smooth congestion degree Cs (n−1) with the recently calculated smooth congestion degree Cs (n) (step S8).

Cs (n) = a ₂ · C (n) + (1−a ₂ ) · Cs (n−1) −1 (2)

However, when the calculation result of this expression is less than 0, the value of Cs (n) is set to 0. Further, a ₂ is a coefficient represented by 0 <a ₂ ≦ 1, and is, for example, 0.9.

  The smoothness degree Cs (n) calculated by the congestion degree calculation unit 22 is notified to the tracking processing unit 11 (step S9). As will be described in detail later, the tracking processing unit 11 performs a tracking process according to the value of the smoothness congestion degree Cs (n) notified from the congestion degree calculation unit 22.

  The tracking processing unit 11 is configured to identify the tracking target 140 from among the plurality of targets 130 and to perform the tracking processing of the tracking target 140. The tracking target 140 is selected by the operator based on, for example, symbols indicating the plurality of targets 130 displayed on the display 4. The selection command of the tracking target 140 by the operator is issued, for example, when the operator operates an operating device (not shown). In the present embodiment, the tracking processing unit 11 is configured to perform processing on the basis of the XY coordinate system unless otherwise specified.

  The tracking processing unit 11 is configured to calculate the smoothing speed Vs (n) of the tracking target 140 at the n scanning time point (latest scanning time point). The tracking processing unit 11 is configured to display the smoothing speed Vs (n) on the display 4.

  The tracking processing unit 11 includes a feature information memory 12, a selection unit 13, an association unit 14, and a motion estimation unit 15.

  The feature information memory 12 is configured to store data output from the signal processing unit 9 and the echo detection unit 10. The feature information memory 12 stores feature information data 201 to 212 for all target echo images 120 at each time point from the (n-T) scan time point to the n scan time point. The constant T is a preset value, for example, about several tens.

  The sorting unit 13 is configured to perform an echo sorting process. Specifically, the selection unit 13 specifies an area where the observation position Xo (n) (the tracking representative point of the tracking target 140) is estimated to exist at the n scanning time point (latest scanning time point).

  Specifically, the sorting unit 13 refers to the coordinate data of the predicted position Xp (n) calculated by the motion estimation unit 15 for the tracking target 140. That is, the selection unit 13 acquires the coordinate data of the predicted position Xp (n) where the tracking representative point P is estimated to exist at the n scan time point. The selection unit 13 sets a selection region S (n) centered on the predicted position Xp (n). The selection region S (n) is, for example, a circular region centered on the predicted position Xp (n). The associating unit 14 searches the selection area S (n).

Here, the selection unit 13 determines the range of the selection region S (n) according to the value of the smoothness congestion level Cs (n) calculated by the congestion level calculation unit 22. Specifically, sorting unit 13, for example, as an example, when smoothness congestion Cs (n) is a predetermined threshold value N ₄ or less, to set the radius of the selection area S (n) as r1. On the other hand, selection unit 13, if the smoothness congestion degree Cs (n) exceeds a predetermined threshold value _{N 4,} the radius of the selection area S (n), is set as r2 smaller than r1.

  The associating unit 14 tracks the observed position Xo (n) of the tracking target 140 from the tracking representative points P of one or more target echo images existing in the selection area S (n). It is specified based on the likelihood of each tracking representative point P calculated from target information (echo area, position error, etc.).

Specifically, for example, the associating unit 14 calculates a plurality of likelihoods Lh _n (n = 1, 2,...) For each tracking representative point P, and the combined likelihood obtained by combining these likelihoods Lh _n is calculated. The highest tracking representative point P is output to the motion estimation unit 15 as the observation position Xo (n) of the tracking target 140. The likelihood Lh _n described above is the degree that each tracking representative point P indicates the likelihood of being the tracking representative point P of the tracking target, and the tracking target feature amount and the selection area S (n ) Is calculated based on the feature amount of each target existing in parentheses. An example of the feature amount here is an echo area of a target.

In the associating unit 14, a threshold value Na _n (n = 1, 2,...) Is set corresponding to each likelihood Lh _n . Then, the associating unit 14, the respective likelihood Lh _n of tracking representative point P as compared to the corresponding threshold Na _n, according to the comparison result, the tracking representative point P is, the tracking target object tracking representative point P It is determined whether or not it can be a candidate. Specifically, when the likelihood Lh _{n at} a certain tracking representative point P existing in the selection area S (n) is _{equal to} or greater than the threshold value Nan, the associating unit 14 tracks the tracking representative point P as a tracking target. A tracking representative point that can be a candidate for the representative point P is determined. On the other hand, if the likelihood Lh _n in Track representative point P which said the there is less than the threshold Na _n, the tracking representative point P, excluded from the candidates of the tracking representative point P of the tracking target object.

Here, the association unit 14 determines the value of the threshold value Na _n according to the value of the smoothness congestion Cs (n). Specifically, the associating unit 14, for example, when smoothness congestion Cs (n) is ₅ or greater than a predetermined threshold value _N, increase the value of the threshold Na _n. On the other hand, if the smoothness congestion Cs (n) is smaller than the predetermined threshold value _{N 5,} to lower the threshold value Na _n.

  The motion estimation unit 15 is configured to perform tracking processing of the tracking target 140 in the XY coordinate system. In the present embodiment, the motion estimation unit 15 performs the tracking process of the tracking target 140 using a coordinate system in which the orientation with respect to the surface of the earth is constant.

  The motion estimator 15 is provided for smoothing the influence of observation errors (errors caused by vibrations or the like generated when the antenna 5 rotates). In the present embodiment, the motion estimation unit 15 performs α-β filter processing as tracking filter processing. The motion estimation unit 15 is configured to calculate a predicted position Xp (n), a smooth position Xs (n), and a smooth speed Vs (n) of the tracking target 140.

Specifically, the motion estimation unit 15 calculates the following formulas (3), (4), and (5).
Predicted position Xp (n) = Xs (n−1) + T × Vs (n−1) (3)
Smooth position Xs (n) = Xp (n) + α {Xo (n) −Xp (n)} (4)
Smoothing speed Vs (n) = Vs (n−1) + (β / T) {Xo (n) −Xp (n)} (5)

The smooth position Xs (n) indicates a position associated with the predicted position Xp (n) and the observation position Xo (n) at the n-scan time point. The smooth position Xs (n) indicates a position where the tracking representative point P1 of the tracking target 140 is estimated to have reached at the n scan time point. The smoothing speed Vs (n) indicates the estimated speed of the tracking representative point P1 at the n scan time point.

  T is the time that elapses from when the motion estimation unit 15 performs the previous smoothing process until the smoothing process is performed, and corresponds to the time required for one scan. Α is a gain used to calculate the smooth position Xs (n). β is a gain used to calculate the smoothing speed Vs (n).

  The gain α has a component αx in the X-axis direction and a component αy in the Y-axis direction. The gain α can also be expressed as a gain α (αx, αy). The component αx is used when calculating the component in the X-axis direction in the above equation (4). The component αy is used when calculating the component in the Y-axis direction in the above equation (4).

  The gain β has a component βx in the X-axis direction and a component βy in the Y-axis direction. The gain β can also be expressed as a gain β (βx, βy). The component βx is used when calculating the component in the X-axis direction in the above equation (5). The component βy is used when calculating the component in the Y-axis direction in the above equation (5).

  The smooth position Xs (n) is located on a line segment LS1 connecting the predicted position Xp (n) and the observation position Xo (n).

  According to the above configuration, the smoothed position Xs (n) is closer to the predicted position Xp (n) as the gain α is smaller. Further, the smaller the gain β, the smaller the amount of change in the smoothing speed Vs (n). For this reason, the degree of smoothing of the calculation result in the motion estimation unit 15 increases, and the amount of variation due to the observation error of the tracking target 140 decreases. However, the response at the time of reflecting the changing needle movement of the tracking representative point P1 of the tracking target 140 in the calculation result of the movement estimation unit 15 becomes slower as the gains α and β are smaller.

  On the other hand, as the gain α is larger, the smooth position Xs (n) is closer to the observation position Xo (n). Further, the degree of smoothing the smoothing speed Vs (n) decreases as the gain β increases. For this reason, the motion estimation unit 15 can reflect the needle changing motion of the tracking representative point P1 of the tracking target 140 with high responsiveness in the tracking filter processing. For this reason, the motion estimation unit 15 can improve the followability of the tracking target 140 with respect to the needle changing motion. The changing needle movement is a movement that changes the direction of the tracking target 140. However, the larger the gains α and β, the larger the amount of fluctuation at each scanning time with respect to the smooth position Xs (n) and the smooth speed Vs (n).

Then, the motion estimation unit 15 sets the values of the gains α and β according to the value of the smoothness congestion level Cs (n) notified from the congestion level calculation unit 22. The gains α and β are expressed as follows, for example, according to the relationship between the thresholds N _{1 to} N ₃ (0 <N ₃ <N ₂ <N ₁ ) and the smoothness congestion level Cs (n). Can do.

α = β = 0 (when N ₁ <Cs (n))
α = 0.5α ₁ , β = 0.5β ₁ (when N ₂ <Cs (n) ≦ N ₁ )
α = α ₁ , β = β ₁ (when N ₃ <Cs (n) ≦ N ₂ )
α = 1.5α ₁ , β = 1.5β ₁ (when Cs (n) ≦ N ₃ )
However, α ₁ and β ₁ are predetermined constants.

  As described above, the following effects can be obtained by setting the values of the gains α and β in accordance with the smoothness congestion degree Cs (n).

When the smoothness congestion level is very high (in this embodiment, N ₁ <Cs (n)), it is estimated that a large number of targets exist near the predicted position of the tracking target. In this case, when the association process by the association unit 14 is performed, there is a high possibility that the tracking target will transfer from the target to be originally tracked to another target (so-called transfer may occur). Therefore, under such a situation where there is a high possibility of transfer, it is possible to prevent the transfer of the tracking target by setting the gains α and β to 0 and performing so-called predictive tracking. Note that the predicted tracking is tracking that calculates the predicted position Xp (n) as a smooth position without considering the observation position Xo (n) when calculating the smooth position Xs (n).

When the smoothness congestion level is slightly high (in this embodiment, N ₂ <Cs (n) ≦ N ₁ ), it is estimated that a relatively large number of targets exist near the predicted position of the tracking target. In this case, when the association process by the association unit 14 is performed, the possibility of transfer is slightly increased. Therefore, in such a situation where the possibility of transfer is slightly high, the gains α and β are set to slightly low values (α = 0.5α ₁ , β = 0.5β _{1 in} this embodiment). By performing the tracking process close to the so-called predictive tracking, it is possible to perform the tracking process that can cope with a change in movement of the tracking target to some extent while suppressing the possibility of transfer of the tracking target.

On the other hand, when the smoothness congestion is a value close to 0 (in this embodiment, Cs (n) ≦ N ₃ ), there is almost no target near the predicted position of the tracking target, or the tracking target It is estimated that there is no target near the predicted position. In this case, since the risk of transfer as described above is very low, the values of the gains α and β are set to values larger than normal (α = 1.5α ₁ , β = 1.5β ₁ ). Thus, it is possible to perform a tracking process that improves the tracking performance of the tracking target with respect to the changing needle movement.

Note that when the smoothness congestion level is N ₃ <Cs (n) ≦ N ₂ , the values of the gains α and β are a balance between the reduction of the risk of transfer of the tracking target and the ensuring of the tracking performance of the tracking target. The obtained value (α = α ₁ , β = β ₁ ) is set.

  The motion estimation unit 15 sends the data calculated using the gains α and β appropriately set according to the smoothness congestion level as described above to the selection unit 13, the congestion level calculation processing unit 20, and the display 4. Output. Specifically, the motion estimation unit 15 outputs the coordinate data of the predicted position Xp (n) to the selection unit 13 and the congestion degree calculation processing unit 20. In the sorting unit 13, the data is used for sorting processing at the (n + 1) scan time point. On the other hand, in the congestion degree calculation processing unit 20, the data is used to specify the cell B that is the calculation target of the congestion degree. Further, the motion estimation unit 15 outputs data specifying the smooth position Xs (n) and the smooth speed Vs (n) to the display 4.

  The display 4 is a liquid crystal display capable of color display, for example. The display 4 displays each target echo image 120 on the display screen using the image data of each target echo image 120. Further, the display 4 displays the smoothing speed Vs (n) as an image. As a result, an image indicating the smoothing speed Vs (n) is displayed on the display screen of the display 4 for the tracking target 140 (target echo image 121). The operator of the radar apparatus 1 can confirm the motion state of the tracking target 140 by confirming the radar image displayed on the display 4.

[effect]
As described above, in the tracking processing device 3 according to the present embodiment, the tracking processing unit exists in the area (cell B in the present embodiment) including the predicted position Xp (n) of the tracking target 140. Tracking processing is performed according to the degree of congestion of the target (in the case of the present embodiment, smoothness congestion degree Cs (n)). In this way, it is possible to appropriately control the tracking process according to the possibility that the tracking target originally changes from the target to be tracked to another target (possibility of transfer). Specifically, in the case of the present embodiment, since the predictive tracking is performed under a situation where the possibility of transfer is high, that is, a situation where the smoothness congestion level Cs (n) is high, it is possible to prevent the transfer. On the other hand, in a situation where the possibility of transfer is low, that is, in a situation where the smoothness congestion level Cs (n) is low, tracking processing with high followability with respect to the needle movement of the tracking target is performed.

  Therefore, according to the tracking processing device 3, the tracking target can be accurately tracked regardless of the surrounding environment.

  Further, the tracking processing device 3 calculates the congestion degree (smooth congestion degree Cs (n)) based on the target number of the target existing in the area including the predicted position Xp (n) of the tracking target 140. Therefore, the congestion degree can be calculated appropriately.

  Further, in the tracking processing device 3, the congestion degree (smooth congestion degree Cs (n)) of the cell B including the predicted position Xp (n) to be tracked among the plurality of cells B into which the predetermined area A is divided. It has been calculated. Thereby, since the tracking of the tracking target is performed based on the degree of congestion calculated for a relatively limited area including the tracking target, the tracking target can be tracked more accurately.

  Further, according to the tracking processing device 3, the degree of congestion can be calculated based on the target numbers at a plurality of timings. In this way, for example, as an example, the target number of the target whose echo intensity is around the threshold value calculated as the target is averaged over time, so the degree of congestion varies greatly from timing to timing. Can be suppressed.

  Further, according to the tracking processing device 3, the smoothness congestion degree can be calculated based on the congestion degree at a plurality of timings. In this way, as in the case described above, the congestion degree calculated based on the target number of the target whose echo intensity is around the threshold value calculated as the target is averaged over time. It is possible to suppress the degree of congestion from fluctuating greatly at each timing.

  In the tracking processing device 3, the gains α and β used when the tracking filter processing is performed are set according to the value of the smoothness congestion level Cs (n). Specifically, when the possibility of transfer is high (when the smoothness congestion level Cs (n) is high), the gains α and β are set to low values. Thereby, since so-called predictive tracking is performed, it is possible to reduce the possibility that the tracking target is transferred from the target to be originally tracked to another target. On the other hand, when the possibility of transfer is low (when the smoothness congestion level Cs (n) is low), the gains α and β are set to high values. As a result, it is possible to perform a tracking process with high followability with respect to the changing needle movement of the tracking target.

  Further, in the tracking processing device 3, the area of the selection region S (n) is set according to the value of the smoothness congestion degree Cs (n). Specifically, when the possibility of transfer is high (when the smoothness congestion level Cs (n) is high), the area of the selection region S (n) is set small. Thereby, since the candidate of the target which can become a tracking object can be narrowed down, the danger of transfer can be reduced. On the other hand, when the possibility of transfer is low (when the smoothness congestion Cs (n) is low), the area of the selection region S (n) is set large. As a result, even a tracking target that has changed significantly can be set as a candidate for a target that can be a tracking target, so that tracking processing with high tracking ability with respect to the changing movement of the tracking target can be performed.

Further, the tracking processing unit 3, the value of the threshold Na _n used in the associating unit 14 is set according to the value of the smoothness congestion Cs (n). In the case likely Noriutsuri occurs (if smoothness congestion degree Cs (n) is high), the threshold value Na _n is set larger. Thereby, since the candidate of the target which can become a tracking object can be narrowed down, the danger of transfer can be reduced. On the other hand, when there is a low possibility that Noriutsuri occurs (if smoothness congestion degree Cs (n) is low), the threshold value Na _n is set smaller. As a result, even a tracking target that has changed significantly can be set as a candidate for a target that can be a tracking target, so that tracking processing with high tracking ability with respect to the changing movement of the tracking target can be performed.

[Modification]
As mentioned above, although embodiment of this invention was described, this invention is not limited to these, A various change is possible unless it deviates from the meaning of this invention.

  (1) In the above embodiment, the target number counted during a certain scan is the target number corresponding to each scan. However, the present invention is not limited to this. Specifically, a target value corresponding to each scan may be a value obtained by averaging the target count counted at each scan and the target count counted at other scans before and after the scan. .

(2) In the above embodiment, the values of the gains α and β are set in stages according to the relationship between the value of the smoothness congestion level Cs (n) and the values of the thresholds N _{1 to} N _3. Not. Specifically, the values of the gains α and β may be set so that the values of the gains α and β change linearly according to the value of the smoothness congestion degree Cs (n). The values of the gains α and β can be expressed as follows, for example.

α = ν · α ₂
β = ν · β ₂
However, when N ₁ <Cs (n), ν = 0, and when N ₁ ≧ Cs (n), ν = 1− (Cs (n) −N ₂ ) / (N ₁ −N ₂ ). . Α ₂ and β ₂ are predetermined constants.

Gain alpha, by setting using the formula described above the beta, the gain alpha, beta value when the value of the smoothness congestion Cs (n) is the N ₁ or more, the value of the smoothness congestion Cs (n) It can be changed linearly accordingly. Thereby, the gains α and β values can be set more appropriately according to the value of the smoothness congestion level Cs (n).

In the above embodiment, the range of the selection area S (n), was set stepwise in accordance with the relationship between the value and the threshold value N ₄ of smoothness congestion Cs (n), this shall not apply. Specifically, as in the case described above, the range of the selection region S (n) so that the range of the selection region S (n) changes linearly according to the value of the smoothness congestion degree Cs (n). May be set.

  (3) FIG. 7 is a schematic diagram for explaining a calculation method of the congestion degree calculated by the congestion degree calculation unit 22 of the tracking processing device according to the modification. FIG. 7 shows a cell B near the predicted position Xp (n) of the tracking target among the plurality of cells B (see FIG. 6). In the above embodiment, the smooth target number Ns (n) in the cell B including the predicted position Xp (n) of the tracking target is calculated as the congestion degree C (n). However, the present invention is not limited to this. In this modification, the degree of congestion C (n) is calculated using linear interpolation.

In the present modification, when the congestion degree calculation unit 22 calculates the congestion degree C (n), the following conditions among the smooth target characteristics Ns (n) stored corresponding to the plurality of cells B are satisfied. The smooth target characteristic Ns (n) of the cell B to be filled is used. Specifically, with reference to FIG. 7, the congestion degree C (n) is determined by _four cells B in which the predicted position Xp (n) is included in a square area surrounded by the center points CP _{1 to} CP _4. The smooth target characteristic Ns (n) (Ns ₁ (n) to Ns ₄ (n) in FIG. 7) stored corresponding to each of (B _{1 to} B _{4 in} FIG. 7) is (6). And the congestion degree calculation part 22 of this modification changes the smooth congestion degree C (n-1) memorize | stored until then to the smooth congestion degree C (n) calculated most recently like the case of the said embodiment. Replace and remember.

  C (n) = Ns ′ (n) · ωy + Ns ″ (n) · (1−ωy) (6)

However, Ns ′ (n) = Ns ₁ (n) · (1−ωx) + Ns ₂ (n) · ωx, Ns ″ (n) = Ns ₃ (n) · (1−ωx) + Ns ₄ (n) · Note that ωx is the x-axis direction component of the distance from CP ₁ to Xp (n) with respect to the distance from CP ₁ to CP ₂ (ie, the distance from CP ₃ to CP ₄ ) (ie, (X-axis direction component of the distance from CP ₃ to Xp (n)), and ωy is the CP to the distance from CP ₁ to CP ₃ (ie, the distance from CP ₂ to CP ₄ ). ₃ is the ratio of the y-axis direction component of the distance from ₃ to Xp (n) (that is, the y-axis direction component of the distance from CP ₄ to Xp (n)).

  The smoothness degree C (n) calculated as described above is notified to the tracking processing unit 11 as in the case of the above embodiment. And the tracking process part 11 performs the tracking process according to the value of the smooth congestion degree C (n) similarly to the case of the said embodiment.

  As described above, according to the tracking processing device according to the present modification, the tracking target can be accurately tracked regardless of the surrounding environment, as in the case of the tracking processing device according to the above-described embodiment.

  And according to the tracking processing apparatus concerning this modification, since the congestion degree is calculated using linear interpolation, the congestion degree of the target can be calculated more accurately.

  (4) In the above embodiment, the smooth target characteristic Ns (x, y, n) in the cell B including the predicted position Xp (n) of the tracking target is calculated as the congestion degree C (n). Not limited to. Specifically, the target number N (x, y, n) at the time of n scans may be calculated as the congestion degree C (n) without calculating the smooth target number.

(5) In the embodiment and the variants described above, in accordance with the smoothness congestion degree (or congestion), the range of selection area S (n), the gain alpha, has been controlled value of beta, and the threshold value Na _n However, the present invention is not limited to this, and at least one of them may be controlled as a variable according to the degree of congestion.

DESCRIPTION OF SYMBOLS 1 Radar apparatus 3 Tracking processing apparatus 11 Tracking processing part 22 Congestion degree calculation part

Claims (7)

A tracking processing unit that performs processing for tracking a tracking target;
A congestion degree calculating unit for calculating a degree of congestion that is a degree of congestion of a target existing in an area including the predicted position of the tracking target;
With
The tracking processing device is characterized in that the tracking processing unit sets a gain such that the lower the value of the congestion degree calculated by the congestion degree calculating unit, the higher the followability to the tracking target. The tracking processing device according to claim 1,
The tracking processing unit sets the gain so that the tracking performance to the tracking target increases step by step as the congestion level calculated by the congestion level calculation unit is lower. Processing equipment. In the tracking processing device according to claim 1 or 2,
The tracking processing device, wherein the congestion degree calculating unit calculates the congestion degree based on a target number as the number of the target existing in the area. In the tracking processing device according to claim 3,
An echo distribution generation unit that counts the target number present in each of a plurality of cells into which the area is divided and stores the target number counted for each of the plurality of cells in association with each cell. Further comprising
The tracking processing apparatus, wherein the congestion degree calculation unit calculates the congestion degree of the target existing in the cell including the predicted position of the tracking target. In the tracking processing device according to claim 3 or 4,
The tracking processing device, wherein the congestion degree calculation unit calculates the congestion degree based on the target numbers at a plurality of timings. In the tracking processing apparatus of any one of Claims 1-5,
The congestion degree calculation unit calculates a smooth congestion degree based on the congestion degree at a plurality of timings,
The tracking processing device, wherein the tracking processing unit performs processing for tracking the tracking target according to the value of the smoothness congestion level calculated by the congestion level calculation unit. Performing a process of tracking the tracking target;
Calculating a degree of congestion which is a degree of congestion of a target existing in an area including the predicted position of the tracking target,
The step of performing the tracking process is characterized in that a gain is set so that the followability to the tracking target becomes higher as the value of the congestion degree calculated by the step of calculating the congestion degree is lower. Tracking processing method.

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